دورية أكاديمية
Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells
| العنوان: | Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells |
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| المؤلفون: | Moosung Lee, Young-Ho Lee, Jinyeop Song, Geon Kim, YoungJu Jo, HyunSeok Min, Chan Hyuk Kim, YongKeun Park |
| المصدر: | eLife, Vol 9 (2020) |
| بيانات النشر: | eLife Sciences Publications Ltd, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Medicine LCC:Science LCC:Biology (General) |
| مصطلحات موضوعية: | chimeric antigen receptor T cells, immunological synapse, optical diffraction tomography, deep learning, quantitative phase imaging, Medicine, Science, Biology (General), QH301-705.5 |
| الوصف: | The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2050-084X |
| العلاقة: | https://elifesciences.org/articles/49023Test; https://doaj.org/toc/2050-084XTest |
| DOI: | 10.7554/eLife.49023 |
| الوصول الحر: | https://doaj.org/article/7abfc5b9b260499d9c4820b9af5b284cTest |
| رقم الانضمام: | edsdoj.7abfc5b9b260499d9c4820b9af5b284c |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2050084X |
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| DOI: | 10.7554/eLife.49023 |